Matrixized learning machine with modified pairwise constraints

Zhu, Yong; Wang, Zhe; Gao, Daqi

doi:10.1016/j.patcog.2015.05.023

Cited by 4 publications

(2 citation statements)

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“…To overcome this drawback (Zhu et al. 2015 ), combined the spatial information and the traditional PC and presented a modified PC as where and are two samples, whose labels are and respectively, , and the spatial measure strategy is adopted as where, contains the k-nearest neighbors of the sample , is the coefficient to control the influence of , and is the mean squared mutual distance between each pair samples.…”

Section: Support Vector Machine With Hypergraph-based Pairwise Constrmentioning

confidence: 99%

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Support vector machine with hypergraph-based pairwise constraints

et al. 2016

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Although support vector machine (SVM) has become a powerful tool for pattern classification and regression, a major disadvantage is it fails to exploit the underlying correlation between any pair of data points as much as possible. Inspired by the modified pairwise constraints trick, in this paper, we propose a novel classifier termed as support vector machine with hypergraph-based pairwise constraints to improve the performance of the classical SVM by introducing a new regularization term with hypergraph-based pairwise constraints (HPC). The new classifier is expected to not only learn the structural information of each point itself, but also acquire the prior distribution knowledge about each constrained pair by combining the discrimination metric and hypergraph learning together. Three major contributions of this paper can be summarized as follows: (1) acquiring the high-order relationships between different samples by hypergraph learning; (2) presenting a more reasonable discriminative regularization term by combining the discrimination metric and hypergraph learning; (3) improving the performance of the existing SVM classifier by introducing HPC regularization term. And the comprehensive experimental results on twenty-five datasets demonstrate the validity and advantage of our approach.

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Section: Support Vector Machine With Hypergraph-based Pairwise Constrmentioning

confidence: 99%

“…To overcome this drawback (Zhu et al. 2015 ), designed a new strategy to combine the discrimination metric based on the traditional PC and the Euclidean distance measure together, i.e., the modified pairwise constraints (MPC) method. In MPC, the spatial measure strategy is based on the simple-graph-constructing.…”

Section: Introductionmentioning

confidence: 99%